Demo Data from EDBT 2026 / reviewers in the wild / expert
Tom G. Bloemberg
dblp:168/1142
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2015
0000-0003-3387-9990ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
metabolomics |
0.2 | 1 | 2015 | Fast parametric time warping of peak lists · Bioinform. 2015 |
Bioinformatics and computational biology › metabolomics
peak alignment |
0.2 | 1 | 2015 | Fast parametric time warping of peak lists · Bioinform. 2015 |
Bioinformatics and computational biology
proteomics |
0.2 | 1 | 2015 | Fast parametric time warping of peak lists · Bioinform. 2015 |
Methods — techniques the papers use, named apart from their topics
parametric time warping · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Fast parametric time warping of peak listsabstractUNLABELLED: Alignment of peaks across samples is a difficult but unavoidable step in the data analysis for all analytical techniques containing a separation step like chromatography. Important application examples are the fields of metabolomics and proteomics. Parametric time warping (PTW) has already shown to be very useful in these fields because of the highly restricted form of the warping functions, avoiding overfitting. Here, we describe a new formulation of PTW, working on peak-picked features rather than on complete profiles. Not only does this allow for a much more smooth integration in existing pipelines, it also speeds up the (already among the fastest) algorithm by orders of magnitude. Using two publicly available datasets we show the potential of the new approach. The first set is a LC-DAD dataset of grape samples, and the second an LC-MS dataset of apple extracts. AVAILABILITY AND IMPLEMENTATION: Parametric time warping of peak lists is implemented in the ptw package, version 1.9.1 and onwards, available from Github (https://github.com/rwehrens/ptw) and CRAN (http://cran.r-project.org). The package also contains a vignette, providing more theoretical details and scripts to reproduce the results below. CONTACT: [email protected]. Ron Wehrens, Tom G. Bloemberg, Paul H. C. Eilers |
Bioinform. | 2 |